Go-Explore is a variant of intrinsically motivated goal exploration processes (IMGEP) (surveyed by [1]}) which generally consists of three phases: 1) defining/learning a goal space, 2) sampling an interesting goal from the goal space, and 3) getting to the sampled goal based on knowledge from previous episodes. Regarding the first step, Go-Explore [2]} pre-defines goals as down-scaled states, and the goal space is adaptivly extended by adding new encountered states. Other IMGEP approaches, like Goal-GAN [3]} uses a generative adversarial network to learn a goal space of appropriate difficulty. As an alternative, AMIGo [4]} trains a teacher to propose goals for a student to reach. In this work, we largely follow the ideas of Go-Explore, and pre-define the goal space as the visited state space, since our research questions are directed at the effects of post-exploration.
